Health insurance tax credits, the earned income tax credit, and health insurance coverage of single mothers.

The Omnibus Budget Reconciliation Act of 1990 enacted a refundable tax credit for low-income working families who purchased health insurance coverage for their children. This health insurance tax credit (HITC) existed during tax years 1991, 1992, and 1993, and was then rescinded. A difference-in-differences estimator applied to Current Population Survey data suggests that adoption of the HITC, along with accompanying increases in the Earned Income Tax Credit (EITC), was associated with a relative increase of about 4.7 percentage points in the private health insurance coverage of working single mothers with high school or less education. Also, a difference-in-difference-in-differences estimator, which attempts to net out the possible influence of the EITC increases but which requires strong assumptions, suggests that the HITC was responsible for about three-quarters (3.6 percentage points) of the total increase. The latter estimate implies a price elasticity of health insurance take-up of -0.42.

COST: Cognitive State Test, a brief screening battery for Alzheimer disease in illiterate and literate patients.

BACKGROUND: The aim was to develop a brief screening battery, Cognitive State Test (COST), for detecting the presence of dementia in both illiterate and literate patients and to assess its validity and reliability. METHODS: COST is a cognitive screening tool that consists of almost all cognitive domains. It takes 5-7 minutes to administer, and has a maximum score of 30. Data were obtained from 114 healthy volunteers and 74 Alzheimer dementia (AD) patients. Subjects' age divided into two groups: A1: <65 years; and A2: ≥65 years and their education level divided into three groups: E1: illiterate; E2: 1-5 years; and E3: ≥6 years. For assessing concurrent validity, total COST score was compared to the Clinical Dementia Rating (CDR), the Mini Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and Basic Activities of Daily Living (BADL). Sensitivity and specificity were determined through a discriminant analysis using the Receiver Operating Characteristic (ROC) curves. Internal consistency was measured using Cronbach's coefficient α. RESULTS: For normal and AD subjects, mean age was 64.9±9.8 years (50 women and 64 men) and 67.2±13.2 years (55 women and 19 men), respectively. Schooling ranged from 0-15 years (mean 5.7±4.2 and 3.3±3.8 years, respectively), and 21 and 37 subjects were illiterate, respectively. The COST significantly and positively correlated with MMSE and MoCA, and significantly and inversely correlated with CDR, the Geriatric Depression Scale (GDS), and BADL. In the E1, E2, and E3 education groups, the optimal cut-off points of COST chosen for diagnosis of AD were 23/24 (sensitivity: 81%, specificity: 99%), 24/25 (sensitivity: 75%, specificity: 86%), and 26/27 (sensitivity: 77%, specificity: 84%), respectively. When illiterate and literate subjects were then pooled, the optimal cut-off score of COST was 24/25, which yielded a sensitivity of 81% and a specificity of 87%. Reliability of the COST was good (0.86). CONCLUSION: The COST is a valid and reliable screening battery for detection of dementia both in the illiterate and the literate Alzheimer patients.

Thymoma patients with or without myasthenia gravis have increased Th17 cells, IL-17 production and ICOS expression.

Thymoma associated myasthenia gravis (TAMG) is a small disease subgroup with autoantibodies against the acetylcholine receptor. The aim of this study was to assess the role of T helper (Th) cells in TAMG compared to thymoma patients without MG (TOMA) and healthy controls (HC). Peripheral blood cells were used for intracellular cytokine measurements and phenotyping of CD4+ Th cells. IL-21 and IL-4 productions and peripheral Th cells were higher in TAMG compared to TOMA patients and HC. Increases of ICOS and Th17 population were detected both in TAMG and TOMA groups. Higher IL-10 and Th1 population have been observed related to thymectomy. ICOS expression and Th17 induced by thymoma may contribute to the development of TAMG.

The Turkish translation study of the Cognitive Reserve Index Questionnaire (CRIq).

BACKGROUND: Cognitive reserve (CR) is defined as the ability of individuals to use their brain in a flexible way to cope with brain pathologies and find alternative cognitive strategies. AIM: The aim of this study was to adapt Cognitive Reserve Index Questionnaire (CRIq) for Turkish population. METHODS: The CRIq was administered to 178 healthy participants from 18 to 80 years old. RESULTS: The mean score of total CRI was found to be 97.7 indicating a medium level of CR. In line with the mean score, 68.6% of participants (n = 120) were found to have a medium level of CRI. The elderly group showed lowest total CRI as well as CRI-education, CRI-work, and CRI-leisure scores compared to young and middle-aged groups (p < .05, for all scores). CONCLUSIONS: This study provided the first translated measure to assess CR in Turkish population.

The association between vitamin B12 and plasma homocysteine levels with episodic memory and the volume of memory related brain structures in middle-aged individuals: a retrospective correlational study.

In previous studies, decreased vitamin B12 and increased plasma homocysteine levels were reported as risk factors for dementia. The aim of this study was to clarify this relationship in earlier ages. Twenty-one healthy middle-aged adults (9 females, 12 males) with a mean age of 46.21 ± 7.99 were retrospectively included in the study. A voxel-based morphometry analysis was performed to measure brain volume. Plasma homocysteine, vitamin B12 levels, verbal and non-verbal memory test performances were recorded. Correlation analyses showed that increased plasma homocysteine was associated with lower memory score. Decreased vitamin B12 level was found to be associated with smaller brain volume in temporal regions. These results suggest that vitamin B12 and plasma homocysteine levels are associated with brain and cognition as early as middle adulthood. Future studies are needed to clarify whether they might be utilized as early hematological biomarkers to predict cognitive decline and neural loss.

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and ß, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

Discrimination ability of the Short Test of Mental Status (STMS) compared to the Mini Mental State Examination (MMSE) in the spectrum of normal cognition, mild cognitive impairment, and probable Alzheimer's disease dementia: The Turkish standardization study.

BACKGROUND: The aim of the present study was to standardize the Short Test of Mental Status (STMS) in the general Turkish aging population and to find its discriminative ability along the continuum of normal cognition, mild cognitive impairment (MCI), and probable Alzheimer's disease dementia (probable AD). METHOD: The sample was composed of 161 participants older than 50, of which 56 were cognitively normal (CN), 42 had MCI, and 63 had probable AD. STMS, Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), and Geriatric Depression Scale (GDS) were administered. RESULTS: The mean STMS score in healthy participants was 33.44. With a cutoff score of 32, STMS had a sensitivity of 81% and specificity of 74% to detect participants with MCI, whereas the MMSE did not have an optimal cutoff score to detect MCI. With a cutoff score of 24, STMS had a sensitivity of 88% and specificity of 86% to detect participants with dementia. With a cutoff score of 24, MMSE had a good sensitivity (92%) and specificity (84%), as well. STMS significantly and positively correlated with MMSE, and significantly but inversely correlated with CDR. Reliability of the STMS was good (alpha coefficient =.88). CONCLUSION: The results show that STMS is more sensitive than MMSE and can be used by clinicians to differentiate both normal cognition from MCI and MCI from probable AD.

CD4+ T Cells of Myasthenia Gravis Patients Are Characterized by Increased IL-21, IL-4, and IL-17A Productions and Higher Presence of PD-1 and ICOS.

Myasthenia gravis (MG) is an autoimmune disease mediated by autoantibodies predominantly against the acetylcholine receptor (AChR). Specific T cell subsets are required for long-term antibody responses, and cytokines secreted mainly from CD4+ T cells regulate B cell antibody production. The aim of this study was to assess the differences in the cytokine expressions of CD4+ T cells in MG patients with AChR antibodies (AChR-MG) and the effect of immunosuppressive (IS) therapy on cytokine activity and to test these findings also in MG patients without detectable antibodies (SN-MG). Clinically diagnosed AChR-MG and SN-MG patients were included. The AChR-MG patients were grouped as IS-positive and -negative and compared with age- and sex-matched healthy controls. Peripheral blood mononuclear cells were used for ex vivo intracellular cytokine production, and subsets of CD4+ T cells and circulating follicular helper T (cTfh) cells were detected phenotypically by the expression of the chemokine and the costimulatory receptors. Thymocytes obtained from patients who had thymectomy were also analyzed. IL-21, IL-4, IL-10, and IL-17A productions in CD4+ T cells were increased in AChR-MG compared to those in healthy controls. IS treatment enhanced IL-10 and reduced IFN-γ production in AChR-MG patients compared to those in IS-negative patients. Increased IL-21 and IL-4 productions were also demonstrated in SN-MG patients. Among CD4+ T cells, Th17 cells were increased in both disease subgroups. Treatment induced higher proportions of Th2 cells in AChR-MG patients. Both CXCR5+ and CXCR5- CD4+ T cells expressed higher programmed cell death protein 1 (PD-1) and inducible costimulatory (ICOS) in AChR-MG and SN-MG groups, mostly irrespective of the treatment. Based on chemokine receptors on CXCR5+PD-1+ in CD4+ T (cTfh) cells, in AChR-MG patients without treatment, the proportions of Tfh17 cells were higher than those in the treated group, whereas the Tfh1 cells were decreased compared with those in the controls. The relevance of CXCR5 and PD-1 in the pathogenesis of AChR-MG was also suggested by the increased presence of these molecules on mature CD4 single-positive thymocytes from the thymic samples. The study provides further evidence for the importance of IL-21, IL-17A, IL-4, and IL-10 in AChR-MG. Disease-related CD4+T cells are identified mainly as PD-1+ or ICOS+ with or without CXCR5, resembling cTfh cells in the circulation or probably in the thymus. AChR-MG and SN-MG seem to have some similar characteristics. IS treatment has distinctive effects on cytokine expression.

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls.

Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures.

Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and coefficients contributing to the evaluation of clinical interpretations. ANNs are graphical models structured with node networks interconnected with arcs each of which is expressed in terms of weights discovered throughout the modeling process. Since ANNs have a complex structure with its layers and nodes in the layers, which provides ANNs the ability to model any data with complex relationships. Among the various models having origins in statistics and computer science, LR and ANNs have prevailed in the area of mass medical data classification. In this study, we introduce the 2 aforementioned approaches in order to generate a model dichotomizing 75 opioid-dependent patients and 59 control subjects from each other. Quantitative electroencephalography (QEEG) absolute power value of each electrode were calculated for 4 consecutive frequency bands namely delta, theta, alpha, and beta with the frequencies, 0.5 to 4, 4 to 8, 8 to 12, and 12 to 20 Hz, respectively. Significant independent variables contributing to the classification were underlined in LR while a feature selection (FS) method, genetic algorithm, is being applied to the ANN model to reveal more informative features. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores, and Gini coefficient. Although ANN-based classifier outperformed compared with LR, both models performed satisfactorily for absolute power measure in beta frequency band. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies.

Reward Processing Deficits During a Spatial Attention Task in Patients With ADHD: An fMRI Study.

OBJECTIVE: In this study, we aimed to explore how cues signaling rewards and feedbacks about rewards are processed in ADHD. METHOD: Inside the scanner, 16 healthy children and 19 children with ADHD completed a spatial attention paradigm where cues informed about the availability of reward and feedbacks were provided about the earned reward. RESULTS: In ventral anterior thalamus (VA), the controls exhibited greater activation in response to reward-predicting cues, as compared with no-reward cues, whereby in the ADHD group, the reverse pattern was observed (nonreward > reward). For feedbacks; absence of rewards produced greater activation than presence in the left caudate and frontal eye field for the control group, whereas for the ADHD group, the reverse pattern was again observed (reward > nonreward). DISCUSSION: The present findings indicate that ADHD is associated with difficulty integrating reward contingency information with the orienting and regulatory phases of attention.

The Use of Quantitative EEG for Differentiating Frontotemporal Dementia From Late-Onset Bipolar Disorder.

The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies.

Medication Effects on EEG Biomarkers in Attention-Deficit/Hyperactivity Disorder.

EEG biomarkers have become increasingly used to aid in diagnosis of attention-deficit/hyperactivity disorder (ADHD). Despite several studies suggesting that EEG theta/beta ratio may help discriminating ADHD from other disorders, the effect of medications on theta/beta ratio is not known. Forty-three children with ADHD that were evaluated with quantitative EEG before and after methylphenidate were included in the study. Theta/beta ratio, theta and beta powers for whole brain, central, and frontal areas were calculated. Theta/beta power decreased significantly after treatment; however, this change was largely due to an increase in beta power, rather than a fall in theta power. The results suggest that beta power is sensitive to medication effects, while theta power remains as a trait biomarker unaffected by medication status. The value of EEG biomarkers for monitoring neuropsychological performance and clinical status should be explored by future studies.

A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders.

Feature selection (FS) and classification are consecutive artificial intelligence (AI) methods used in data analysis, pattern classification, data mining and medical informatics. Beside promising studies in the application of AI methods to health informatics, working with more informative features is crucial in order to contribute to early diagnosis. Being one of the prevalent psychiatric disorders, depressive episodes of bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD), leading to suboptimal therapy and poor outcomes. Therefore discriminating MDD and BD at earlier stages of illness could help to facilitate efficient and specific treatment. In this study, a nature inspired and novel FS algorithm based on standard Ant Colony Optimization (ACO), called improved ACO (IACO), was used to reduce the number of features by removing irrelevant and redundant data. The selected features were then fed into support vector machine (SVM), a powerful mathematical tool for data classification, regression, function estimation and modeling processes, in order to classify MDD and BD subjects. Proposed method used coherence, a promising quantitative electroencephalography (EEG) biomarker, values calculated from alpha, theta and delta frequency bands. The noteworthy performance of novel IACO-SVM approach stated that it is possible to discriminate 46 BD and 55 MDD subjects using 22 of 48 features with 80.19% overall classification accuracy. The performance of IACO algorithm was also compared to the performance of standard ACO, genetic algorithm (GA) and particle swarm optimization (PSO) algorithms in terms of their classification accuracy and number of selected features. In order to provide an almost unbiased estimate of classification error, the validation process was performed using nested cross-validation (CV) procedure.

Epigenetics of multiple sclerosis: an updated review.

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease characterized with autoimmune response against myelin proteins and progressive axonal loss. The heterogeneity of the clinical course and low concordance rates in monozygotic twins have indicated the involvement of complex heritable and environmental factors in MS pathogenesis. MS is more often transmitted to the next generation by mothers than fathers suggesting an epigenetic influence. One of the possible reasons of this parent-of-origin effect might be the human leukocyte antigen-DRB1*15 allele, which is the major risk factor for MS and regulated by epigenetic mechanisms such as DNA methylation and histone deacetylation. Moreover, major environmental risk factors for MS, vitamin D deficiency, smoking and Ebstein-Barr virus are all known to exert epigenetic changes. In the last few decades, compelling evidence implicating the role of epigenetics in MS has accumulated. Increased or decreased acetylation, methylation and citrullination of genes regulating the expression of inflammation and myelination factors appear to be particularly involved in the epigenetics of MS. Although much less is known about epigenetic factors causing neurodegeneration, epigenetic mechanisms regulating axonal loss, apoptosis and mitochondrial dysfunction in MS are in the process of identification. Additionally, expression levels of several microRNAs (miRNAs) (e.g., miR-155 and miR-326) are increased in MS brains and potential mechanisms by which these factors might influence MS pathogenesis have been described. Certain miRNAs may also be potentially used as diagnostic biomarkers in MS. Several reagents, especially histone deacetylase inhibitors have been shown to ameliorate the symptoms of experimental allergic encephalomyelitis. Ongoing efforts in this field are expected to result in characterization of epigenetic factors that can be used in prediction of treatment responsive MS patients, diagnostic screening panels and treatment methods with specific mechanism of action.

EEG power, cordance and coherence differences between unipolar and bipolar depression.

INTRODUCTION: Understanding the biological underpinnings of unipolar (UD) and bipolar depression (BD) is vital for avoiding inappropriate treatment through the misdiagnosis of bipolar patients in their first depressive episode. One plausible way to distinguish between UD and BD is to compare EEG brain dynamics to identify potential neurophysiological biomarkers. Here we aimed to test group differences in EEG power, cordance and coherence values between UD and BD. METHODS: Twenty-five bipolar and 56 unipolar depression patients were recruited. Sociodemographic and clinical variables were collected in addition to resting state EEG. Data was analyzed with multivariate and repeated analyses of variance where parametric assumptions were met. RESULTS: Accordingly, we did not find any differences in the EEG absolute power and frontal asymmetry indexes between UD and BD. Regarding cordance, significant group differences were observed in the right theta cordance values (p=0.031). Regarding coherence, BD patients (as compared to UD) exhibited greater central-temporal theta (p=0.003), and parietal-temporal alpha (p=0.007) and theta (p=0.001) coherence. Lastly, less alpha coherence in BD was present at right frontal-central (p=0.007) and central inter-hemispheric (p=0.019) regions. CONCLUSIONS: Our results demonstrate that EEG cordance and coherence values have potential to discriminate between UD and BD. The loss of temporal synchronization in the frontal interhemispheric and right sided frontolimbic neuronal networks may be a unique feature that distinguishes between BD and UD.